Incremental-Learning-Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations

Deshmukh, Raj; Hwang, Inseok

doi:10.2514/1.i010711

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…While the precise definition of an air traffic flow depends on the application, it is generally considered a pattern of air traffic in the spatial and, in some applications, temporal dimensions. A large portion of aviation research focuses on the spatial dimension of air traffic flows [11,[29][30][31][32][33][34][35][36][37][38]. However, it is occasionally observed that there are trajectories of flights that do not appear to belong to any specific air traffic flow.Much of the literature related to identifying air traffic flows reports a certain percentage of the trajectories detected as outliers, where these trajectories may be considered spatial anomalies.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Specifically, Puranik & Mavris [19] and Puranik [4] presented a method leveraging the DBSCAN algorithm and Support Vector Machines (SVMs) to detect anomalies in departing and arriving general aviation flights. In the context of commercial operations, Kim & Hwang [33], Deshmukh & Hwang [11,30], and Deshmukh [32] proposed TempAD, an algorithm designed to provide formulas related to the bounds of normality that are easily interpreted in natural languages, where this method utilizes DBSCAN to identify air traffic flows as a data pre-processing step. TempAD has been utilized to detect anomalies in the vertical dimension (altitude), the speed dimension (ground speed), and energy metrics such as specific total energy and specific potential energy rate [11,30,32].…”

Section: Energy Anomaly Detectionmentioning

confidence: 99%

“…In the context of commercial operations, Kim & Hwang [33], Deshmukh & Hwang [11,30], and Deshmukh [32] proposed TempAD, an algorithm designed to provide formulas related to the bounds of normality that are easily interpreted in natural languages, where this method utilizes DBSCAN to identify air traffic flows as a data pre-processing step. TempAD has been utilized to detect anomalies in the vertical dimension (altitude), the speed dimension (ground speed), and energy metrics such as specific total energy and specific potential energy rate [11,30,32]. Jarry et al [55] presented a method to detect trajectories as being anomalous in energy metrics utilizing a Functional Principal Components Analysis (FPCA)-based approach combined with a sliding window and outlier scoring.…”

Section: Energy Anomaly Detectionmentioning

confidence: 99%

“…to detect a high or low energy landing as anomalous, an anomaly detection algorithm would likely require an entire trajectory's energy profile. As indicated, the metrics selected for energy anomaly detection include SPE, SKE, and STER, as combinations of these metrics have been widely used in aviation energy anomaly detection studies [4,11,28,[30][31][32]53]. To prevent bias in the DBSCAN clustering due to having metrics of varying magnitudes, a normalization is performed prior to applying DBSCAN.…”

Section: Energy Anomaly Detectionmentioning

confidence: 99%

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

Corrado

Puranik

Fischer

et al. 2021

Transportation Research Part C: Emerging Technologies

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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.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Energy Anomaly Detectionmentioning

confidence: 99%

Section: Energy Anomaly Detectionmentioning

confidence: 99%

Section: Energy Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

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

Corrado

Puranik

Fischer

et al. 2021

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…A key field of focus in these studies has been the development of a more proactive and predictive model for the identification of risk, to be used in conjunction with existing frameworks of flight safety investigation which mostly rely on the analysis of past incidents and accidents. Commonly addressed areas of study include anomaly detection in time series data [3][4][5], precursor identification [6][7][8] and clustering of in-flight event data [9], as well as the analysis of text-based flight narratives. Each of these approaches tends to rely on different sources of flight safety data which vary in accessibility depending on the sensitivity of the information contained.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives

2020

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The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.

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Temporal logic learning-based anomaly detection in metroplex terminal airspace operations

Deshmukh¹,

Sun²,

Kim³

et al. 2021

Transportation Research Part C: Emerging Technologies

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Incremental-Learning-Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations

Cited by 14 publications

References 13 publications

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

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

Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives

Temporal logic learning-based anomaly detection in metroplex terminal airspace operations

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