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.

/pdf/a-clustering-based-quantitative-analysis-of-the-v5tao3x9tn.pdf

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

This study applied machine learning technique to predict the occurrences of Missed Approach (MA) at an airport. In general, MA events are rare, and they typically occur at an aircraft’s descending phase a few minutes before landing. The occurrence of MA is usually indicative of safety-related concerns; however, it may cause excessive delays due to disrupting the arrival sequence during peak hours. This study focused on Denver International Airport (DEN) since MA events occurred more frequently as compared with other airports. Instead of using on-board data of individual aircraft, which is difficult to obtain systematically in real time, this study relied on airport operational data and developed a machine learning model to predict the likelihood of MA occurrences for DEN. The results of this study can inform the design and development of a real-time alerting tool for terminal or tower air traffic managers to take pre-emptive actions for mitigating the impact on terminal efficiency and increasing airport throughput. Lessons learned through this study can be applied to other airports with MA concerns.

A Machine Learning Application for Predicting and Alerting Missed Approaches for Airport Management

Mid-air collision risk estimation is crucial for maintaining aviation safety and improving the efficiency of air traffic procedures. This paper introduces a novel, data-driven methodology for estimating the probability of mid-air collisions between aircraft by combining Monte Carlo simulation and the Peaks Over Threshold approach from Extreme Value Theory. This innovative approach has substantial advantages over traditional methods. Firstly, it reduces the number of assumptions about the traffic flow compared to traditional analytical methods. In fact, data-driven techniques require fewer assumptions, as they inherently capture the structures of the traffic flow within the underlying data. Secondly, it converges faster than methods based on crude Monte Carlo simulation. Notably, by employing Extreme Value Theory, this approach enables efficient evaluation of low-probabilities, which are commonly found in collision risk modelling. The effectiveness of the proposed methodology is demonstrated through estimating the probability of a mid-air collision in a real-world practical example. The case study investigates the risk of collisions between departures and go-arounds in the terminal airspace at Zurich Airport, highlighting the potential for improved safety and efficiency in air traffic management. 

Data-driven mid-air collision risk modelling using extreme-value theory

: The analysis and prediction of go-arounds, also referred to as missed approaches, is an active ﬁeld of research due to the go-around’s impact on safety and the disruption of the trafﬁc ﬂow at airports. The advent of open-source aircraft trajectories available to researchers has increased the level of interest in the ﬁeld. This paper introduces a publicly available dataset containing metadata of almost 9 million landings and 33,000 go-arounds. The dataset is based on observations from the OpenSky Network and includes data from 176 airports in 44 countries observed in the year 2019. After downloading the data, a go-around classiﬁcation was performed and the quality was assessed. The usefulness of the dataset is illustrated with two novel example applications. The ﬁrst example shows how the go-around rate for a runway can be modeled by using a quasi-binomial generalized linear model, while the second example compares the go-around rates for a number of airport–airline pairs. The introduced dataset is signiﬁcantly larger than the data used so far in the analysis of go-arounds and provides the opportunity to develop novel use cases. This dataset frees researchers from having to collect and process large amounts of data and instead lets them focus on the analysis. The authors are convinced that this large dataset will stoke the creativity of the research community and facilitate interesting and novel applications.

/pdf/large-landing-trajectory-dataset-for-go-around-analysis-2hv11w95.pdf

Large Landing Trajectory Dataset for Go-Around Analysis

Airspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the Variational Autoencoder structure to the problem of 4-dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a Variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories. 

Deep generative modelling of aircraft trajectories in terminal maneuvering areas

Go-arounds (GAs) are standard air traffic control procedures during which aircraft approach a runway but do not land. The incidence of a GA can subsequently affect the workload of flight crews and air traffic controllers, and might impact an airport runway’s throughput capacity. In this study, two different modeling methods for predicting the occurrence of GAs based on open-source Automatic Dependent Surveillance–Broadcast (ADS-B) and meteorological data are presented. A macroscopic model quantifies the probability of a GA within the next hour for an airport by applying a generalized additive model. A microscopic model employs a number of machine learning classifiers on trajectories of aircraft on approach in order to predict if a GA will be performed. Even though the results of the macroscopic model are promising, the information currently available to predict the probability of a GA is not detailed enough to achieve satisfactory predictions. Similarly, the microscopic model is capable of predicting 50% of all GAs, with false positive rate below 7%. Despite the limitations of the quality of the results, the authors are convinced that both modeling methods can be inspiring to other researchers and provide useful insights into the airport system under scrutiny.

/pdf/predicting-airplane-go-arounds-using-machine-learning-and-178p6pdxp3.pdf

Predicting airplane go-arounds using machine learning and open-source data

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.

/pdf/combined-multilateration-with-machine-learning-for-enhanced-22mwysy1g7.pdf

Combined multilateration with machine learning for enhanced aircraft localization

Navigating Interference - Examining In-Flight GNSS Spoofing Patterns and Signal Disruptions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Raphael Monstein

Author Tools

Chat about Author