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Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction...
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Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 31144

Special Issue Editors

Dr. Manuel Soler

E-Mail Website
Guest Editor
Aerospace Engineering Research Group, Universidad Carlos III de Madrid, 28005 Madrid, Spain
Interests: low-thrust trajectory optimization; commercial aircraft trajectory optimization; meteorological uncertainty and air traffic management (ATM); aviation and climate impact
Prof. Dr. Jacco M. Hoekstra

E-Mail Website
Guest Editor
Control and Simulation Research Group, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: aeronautics; flight simulation; avionics; ATM

Special Issue Information

Dear Colleagues,

Flying the ideal, optimal trajectory and vertical profile has been the holy grail of ATM research for decades. For this purpose, a lot of this research has been oriented towards a concept called Trajectory-Based Operations (TBO). In TBO, the trajectory becomes the fundamental element of the ATM system. The current ATM system is based on more tactical clearances. TBO should provide the capabilities, decision support tools, and automation to manage aircraft movement by trajectory. This shift from clearance-based to trajectory-based ATC should enable aircraft to plan and fly negotiated so-called business trajectories.

At the planning level, TBO aims at more efficient and environmentally friendly flight planning concepts, reducing airlines operating costs, allowing a climatic-friendly ATM system, while at the same time increasing the capacity of the system without jeopardizing its safety. Specific research domains within aircraft trajectory optimization with open questions include (but it is not limited to): the consideration of uncertainties in trajectory optimization, the assessment and minimization of climatic impact in aircraft operations, the modelling and resolution of multi-aircraft problems leading to system-wide solutions that are stable and resilient.

At the execution level, the question remains whether the extra investment, effort and communication are worth the yet unknown benefits. What if it becomes merely a more verbose clearance-based system, in which trajectory updates are as frequent as waypoint passing? Therefore, the key issue for solving conflicts or sequencing problems is a very high predictability. For research, one of the questions to address thus is: is this high predictability feasible and which methods are available to improve it.  

The mismatch between planned and actual trajectories caused, among others, by inherent uncertainties arising in aircraft operations, including airports, air traffic control interventions, and unavailable information, e.g., the cost index, and the take-off weight, constitute significant gaps that the scientific community need to tackle. The exploitation of data by means of artificial intelligence and causal models can lead to novel trajectory prediction approaches, which could facilitate the transition towards the TBO paradigm. Open research domains include novel approaches to propagate aircraft trajectory uncertainties, the deepen into artificial intelligence techniques for enhancing the prediction of aircraft trajectories, and the aggregation of different sources of uncertainties in trajectory predictions, notably weather via Ensemble Probabilistic Forecasts.

This special issue intends to bring recognition to the contribution of aircraft trajectory optimization and aircraft trajectory prediction techniques and will provide a forum to disseminate the latest research work with the aim of further stimulating interest in this area of great potential.

Potential topics include but are not limited to the following:

  • Robust aircraft trajectory optimization.
  • Aircraft trajectory optimization and climate change.
  • Uncertainty propagation in trajectory prediction.
  • Artificial Intelligence techniques applied to aircraft trajectory optimization
  • Artificial Intelligence techniques applied to trajectory prediction
  • Stable and resilient solutions to the ATM-System

Dr. Manuel Soler
Prof. Dr. Jacco M. Hoekstra
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.

Published Papers (6 papers)

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Research

Jump to: Review

17 pages, 1138 KiB  
Article
Air Traffic Complexity Map Based on Linear Dynamical Systems
by Daniel Delahaye, Adrían García, Julien Lavandier, Supatcha Chaimatanan and Manuel Soler
Aerospace 2022, 9(5), 230; https://doi.org/10.3390/aerospace9050230 - 22 Apr 2022
Cited by 9 | Viewed by 2504
Abstract
This paper presents a new air traffic complexity metric based on linear dynamical systems, of which the goal is to quantify the intrinsic complexity of a set of aircraft trajectories. Previous works have demonstrated that the structure and organization of air traffic are [...] Read more.
This paper presents a new air traffic complexity metric based on linear dynamical systems, of which the goal is to quantify the intrinsic complexity of a set of aircraft trajectories. Previous works have demonstrated that the structure and organization of air traffic are essential factors in the perception of the complexity of an air traffic situation. Usually, they were not able to explicitly address trajectory pattern organization. The new metric, by identifying the organization properties of trajectories in a traffic pattern, captures some of the key factors involved in ATC complexity. The key idea of this work is to find a linear dynamical system which fits a vector field as closely as possible to the observations given by the aircraft positions and speeds. This approach produces an aggregate complexity metric that enables one to identify high (low) complexity regions of the airspace and compare their relative complexity. The metric is very appropriate to compare different traffic situations for any scale (sector or country) by associating a complexity index to each trajectory sample in the airspace. For instance, to compute the complexity for a sector, one must just sum-up the complexity for trajectory samples intersecting such a sector. This computation can also be extended in the time dimension in order to estimate the average complexity in a given airspace for a period of time. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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20 pages, 14759 KiB  
Article
Trajectory Clustering for Air Traffic Categorisation
by Tatjana Bolić, Lorenzo Castelli, Andrea De Lorenzo and Fulvio Vascotto
Aerospace 2022, 9(5), 227; https://doi.org/10.3390/aerospace9050227 - 21 Apr 2022
Cited by 3 | Viewed by 2725
Abstract
Availability of different types of data and advances in data-driven techniques open the path to more detailed analyses of various phenomena. Here, we examine the insights that can be gained through the analysis of historical flight trajectories, using data mining techniques. The goal [...] Read more.
Availability of different types of data and advances in data-driven techniques open the path to more detailed analyses of various phenomena. Here, we examine the insights that can be gained through the analysis of historical flight trajectories, using data mining techniques. The goal is to learn about usual (or nominal) choices airlines make in terms of routing, and their relation with aircraft types and operational flight costs. The clustering is applied to intra-European trajectories during one entire summer season, and a statistical test of independence is used to evaluate the relations between the variables of interest. Even though about half of all flights are less than 1000 km long, and mostly operated by one airline, along one trajectory, the analysis shows that, for longer flights, there exists a clear relation between the trajectory clusters and the operating airlines (in about 49% of city pairs) and/or the aircraft types (30%), and/or the flight costs (45%). Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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23 pages, 6134 KiB  
Article
Fundamental Framework to Plan 4D Robust Descent Trajectories for Uncertainties in Weather Prediction
by Shumpei Kamo, Judith Rosenow, Hartmut Fricke and Manuel Soler
Aerospace 2022, 9(2), 109; https://doi.org/10.3390/aerospace9020109 - 17 Feb 2022
Cited by 3 | Viewed by 2761
Abstract
Aircraft trajectory planning is affected by various uncertainties. Among them, those in weather prediction have a large impact on the aircraft dynamics. Trajectory planning that assumes a deterministic weather scenario can cause significant performance degradation and constraint violation if the actual weather conditions [...] Read more.
Aircraft trajectory planning is affected by various uncertainties. Among them, those in weather prediction have a large impact on the aircraft dynamics. Trajectory planning that assumes a deterministic weather scenario can cause significant performance degradation and constraint violation if the actual weather conditions are significantly different from the assumed ones. The present study proposes a fundamental framework to plan four-dimensional optimal descent trajectories that are robust against uncertainties in weather-prediction data. To model the nature of the uncertainties, we utilize the Global Ensemble Forecast System, which provides a set of weather scenarios, also referred to as members. A robust trajectory planning problem is constructed based on the robust optimal control theory, which simultaneously considers a set of trajectories for each of the weather scenarios while minimizing the expected value of the overall operational costs. We validate the proposed planning algorithm with a numerical simulation, assuming an arrival route to Leipzig/Halle Airport in Germany. Comparison between the robust and the inappropriately-controlled trajectories shows the proposed robust planning strategy can prevent deteriorated costs and infeasible trajectories that violate operational constraints. The simulation results also confirm that the planning can deal with a wide range of cost-index and required-time-of-arrival settings, which help the operators to determine the best values for these parameters. The framework we propose is in a generic form, and therefore it can be applied to a wide range of scenario settings. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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24 pages, 2507 KiB  
Article
Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints
by Ramon Dalmau, Xavier Prats and Brian Baxley
Aerospace 2021, 8(12), 377; https://doi.org/10.3390/aerospace8120377 - 04 Dec 2021
Cited by 3 | Viewed by 2290
Abstract
The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a [...] Read more.
The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a reiterated update of the optimal trajectory during the descent, provides excellent environmental impact mitigation figures while meeting operational constraints in the presence of modeling errors. Despite that, the computational delay associated with the solution of the trajectory optimization problem could lead to performance degradation and stability issues. This paper proposes two guidance strategies based on the theory of neighboring extremals that alleviate this problem. Parametric sensitivities are obtained by linearization of the necessary conditions of optimality along the active optimal trajectory plan to rapidly update it for small perturbations, effectively converting the complex and time consuming non-linear programming problem into a manageable quadratic programming problem. Promising results, derived from more than 4000 simulations, show that the performance of this method is comparable to that of instantaneously recalculating the optimal trajectory at each time sample. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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Review

Jump to: Research

32 pages, 1166 KiB  
Review
A Comprehensive Survey on Climate Optimal Aircraft Trajectory Planning
by Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Sigrun Matthes, Volker Grewe, Simone Dietmüller, Sabine Baumann, Hiroshi Yamashita, Feijia Yin, Federica Castino, Florian Linke, Benjamin Lührs and Maximilian Mendiguchia Meuser
Aerospace 2022, 9(3), 146; https://doi.org/10.3390/aerospace9030146 - 07 Mar 2022
Cited by 22 | Viewed by 4699
Abstract
The strong growth rate of the aviation industry in recent years has created significant challenges in terms of environmental impact. Air traffic contributes to climate change through the emission of carbon dioxide (CO2) and other non-CO2 effects, and the associated [...] Read more.
The strong growth rate of the aviation industry in recent years has created significant challenges in terms of environmental impact. Air traffic contributes to climate change through the emission of carbon dioxide (CO2) and other non-CO2 effects, and the associated climate impact is expected to soar further. The mitigation of CO2 contributions to the net climate impact can be achieved using novel propulsion, jet fuels, and continuous improvements of aircraft efficiency, whose solutions lack in immediacy. On the other hand, the climate impact associated with non-CO2 emissions, being responsible for two-thirds of aviation radiative forcing, varies highly with geographic location, altitude, and time of the emission. Consequently, these effects can be reduced by planning proper climate-aware trajectories. To investigate these possibilities, this paper presents a survey on operational strategies proposed in the literature to mitigate aviation’s climate impact. These approaches are classified based on their methodology, climate metrics, reliability, and applicability. Drawing upon this analysis, future lines of research on this topic are delineated. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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19 pages, 1228 KiB  
Review
Aircraft 4D Trajectory Prediction in Civil Aviation: A Review
by Weili Zeng, Xiao Chu, Zhengfeng Xu, Yan Liu and Zhibin Quan
Aerospace 2022, 9(2), 91; https://doi.org/10.3390/aerospace9020091 - 10 Feb 2022
Cited by 22 | Viewed by 13123
Abstract
Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces [...] Read more.
Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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