Application of Data Science to Aviation

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (20 June 2021) | Viewed by 23614

Special Issue Editors


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Guest Editor
ONERA DTIS, Université de Toulouse, CEDEX 4, 31055 Toulouse, France
Interests: machine learning; data science; decision science; air traffic management; aviation
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Institute of Flight Systems, Bundeswehr University Munich, 85577 Neubiberg, Germany
Interests: air transportation; data-driven and model-based environments; predictive analysis; integrated airspace and airport management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Future aviation requires air traffic providers, operators, and researchers to implement new procedures and technologies for an efficient and environment-friendly air transportation network. Data analytics and machine learning (ML) techniques are ​well suited for aviation to extract information from the large amounts of generated data​, to predict future situations based on historical information and to assist humans in taking optimal decisions. The rationale is to try to learn how to imitate the behavior of operators rather than having them explain and model an incomplete set of rules they are assumed to follow.

The air transportation system is complex, multidimensional, highly distributed, and interdependent. It interacts with global and regional economies and has reached its limits in many ways. The operational uncertainties related to weather conditions, increasing safety requirements and environmental expectations (green aviation), are challenging the robustness and efficiency of the system and open new research questions. ​​

In order to provide input for a better situation awareness and for collaborative optimization, significant added value stems from various data sources such as flight plans, onboard flight data records, maintenance records, secondary surveillance radar information (trajectories, Mode S, and ADS-B), ground-based augmentation systems (GBAS), weather information, satellite imaging, or stakeholders’ resource planning information.

This Special Issue will focus on the use of aviation-related data (such as the data sources listed above) for Artificial Intelligence and data science techniques (including data analytics, machine learning, reinforcement learning, constraint optimization) in order to improve the operational aviation environment.

Dr. Xavier Olive
Dr. Michael Schultz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aviation
  • big data
  • machine learning
  • artificial intelligence
  • air traffic management
  • air traffic operations

Related Special Issue

Published Papers (5 papers)

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Research

27 pages, 1288 KiB  
Article
An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning
by Stefan Reitmann and Michael Schultz
Aerospace 2022, 9(2), 77; https://doi.org/10.3390/aerospace9020077 - 01 Feb 2022
Cited by 7 | Viewed by 2705
Abstract
Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models [...] Read more.
Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM. Full article
(This article belongs to the Special Issue Application of Data Science to Aviation)
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17 pages, 3165 KiB  
Article
Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting
by Junghyun Kim and Kyuman Lee
Aerospace 2021, 8(9), 236; https://doi.org/10.3390/aerospace8090236 - 26 Aug 2021
Cited by 8 | Viewed by 3381
Abstract
Obtaining reliable wind information is critical for efficiently managing air traffic and airport operations. Wind forecasting has been considered one of the most challenging tasks in the aviation industry. Recently, with the advent of artificial intelligence, many machine learning techniques have been widely [...] Read more.
Obtaining reliable wind information is critical for efficiently managing air traffic and airport operations. Wind forecasting has been considered one of the most challenging tasks in the aviation industry. Recently, with the advent of artificial intelligence, many machine learning techniques have been widely used to address a variety of complex phenomena in wind predictions. In this paper, we propose a hybrid framework that combines a machine learning model with Kalman filtering for a wind nowcasting problem in the aviation industry. More specifically, this study has three objectives as follows: (1) compare the performance of the machine learning models (i.e., Gaussian process, multi-layer perceptron, and long short-term memory (LSTM) network) to identify the most appropriate model for wind predictions, (2) combine the machine learning model selected in step (1) with an unscented Kalman filter (UKF) to improve the fidelity of the model, and (3) perform Monte Carlo simulations to quantify uncertainties arising from the modeling process. Results show that short-term time-series wind datasets are best predicted by the LSTM network compared to the other machine learning models and the UKF-aided LSTM (UKF-LSTM) approach outperforms the LSTM network only, especially when long-term wind forecasting needs to be considered. Full article
(This article belongs to the Special Issue Application of Data Science to Aviation)
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20 pages, 1979 KiB  
Article
Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem
by Micha Zoutendijk and Mihaela Mitici
Aerospace 2021, 8(6), 152; https://doi.org/10.3390/aerospace8060152 - 28 May 2021
Cited by 23 | Viewed by 7977
Abstract
The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay [...] Read more.
The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization. Full article
(This article belongs to the Special Issue Application of Data Science to Aviation)
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32 pages, 857 KiB  
Article
Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit
by Marta Ribeiro, Joost Ellerbroek and Jacco Hoekstra
Aerospace 2021, 8(4), 93; https://doi.org/10.3390/aerospace8040093 - 01 Apr 2021
Cited by 12 | Viewed by 2664
Abstract
Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are [...] Read more.
Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios. Full article
(This article belongs to the Special Issue Application of Data Science to Aviation)
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20 pages, 9043 KiB  
Article
Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach
by Stanley Förster, Michael Schultz and Hartmut Fricke
Aerospace 2021, 8(2), 29; https://doi.org/10.3390/aerospace8020029 - 26 Jan 2021
Cited by 8 | Viewed by 2785
Abstract
The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, [...] Read more.
The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, in particular, the time-to-fly from the turn onto the final approach course to the threshold. The predictions are then used to determine advice for the controller regarding time-to-lose or time-to-gain for optimizing the separation within a sequence of aircraft. Most prediction methods developed so far provide only a point estimate for the time-to-fly. Complementary, we see the need to further account for the uncertain nature of aircraft movement based on a probabilistic prediction approach. This becomes very important in cases where the air traffic system is operated at its limits to prevent safety-critical incidents, e.g., separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that can provide a measure of uncertainty of the prediction not only in form of a prediction interval but also by generating a probability distribution over the dependent variable. While the data preparation, model training, and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate the interpretation of the results and provide a way for deriving advice to the controller from it. With this contribution, there is now a tool available that allows a more sophisticated prediction of time-to-fly, depending on the specific needs of the use case and which helps to separate arriving aircraft more efficiently. Full article
(This article belongs to the Special Issue Application of Data Science to Aviation)
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