Regular graph-based free route flight planning approach

Samolej, Sławomir; Dec, Grzegorz; Rzońca, Dariusz; Majka, Andrzej; Rogalski, Tomasz

doi:10.1108/aeat-11-2020-0272

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…ATCo = air traffic controller; ATM = air traffic management; ATS = air traffic service; BN = Bayesian network; CPA = closest point of approach; dCPA = distance closest point of approach; ETA = event tree analysis; FL = flight level; ft = feet; LoS = loss of separation; NM = nautical miles; SESAR = single European sky ATM research; SMI = separation minima infringement; SPF Among the aspects that most concern the aviation sector today, mainly due to the anticipated growth of traffic and the exhaustion of the current air traffic management (ATM) system, is the loss of separation between aircraft in airspace (Samolej et al, 2021). This research addresses the development of predictive safety models to evaluate the impact of new automation solutions on safety and resilience.…”

Section: Definitions Acronyms and Abbreviationsmentioning

confidence: 99%

“…Among the aspects that most concern the aviation sector today, mainly due to the anticipated growth of traffic and the exhaustion of the current air traffic management (ATM) system, is the loss of separation between aircraft in airspace (Samolej et al , 2021). This research addresses the development of predictive safety models to evaluate the impact of new automation solutions on safety and resilience.…”

Section: Introductionmentioning

confidence: 99%

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Safety performance functions to predict separation minima infringements in en-route airspace

Jurado

Comendador

Suárez

et al. 2022

AEAT

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Purpose The purpose of this study is to establish a systematic framework to characterise the safety of air routes, in terms of separation minima infringements (SMIs) between en-route aircraft, based on the definition of models known as safety performance functions. Design/methodology/approach Techniques with high predictive capability were selected that enable both expert knowledge and data to be harnessed: Bayesian networks. It was necessary to establish a conceptual framework that integrates the knowledge currently available on the causality and precursors of SMIs with the hindsight derived from the analysis of the type of data available. To translate the conceptual framework into a set of causal subnets, the concepts of air traffic management (ATM) barrier model and event trees have been incorporated. Findings The model combines analytics and insights, as well as predictive capability, to answer the question of how airspace separation infringements are produced and what their frequency of occurrence will be. The main outputs of the network are the predicted probability of success for the ATM barriers and the predicted probability distribution of the vertical and horizontal separation of an aircraft in its closest point of approach. Originality/value The main contribution of this work is that, by virtue of the calculation capacity obtained, the network can be used to draw conclusions about the impact that a modification of the airspace and of the traffic, or operational conditions, would have on the effectiveness of the barriers and on the final distributions of distance between aircraft in the CPA, thereby estimating the probability of SMI.

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Section: Definitions Acronyms and Abbreviationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Safety performance functions to predict separation minima infringements in en-route airspace

Jurado

Comendador

Suárez

et al. 2022

AEAT

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“…The number of flights is forecast to almost double by 2035 (EUROCONTROL, 2010). Within this context, Europe has a very complex transport network, specifically air transport, due to the high level of mobility of its inhabitants (Samolej et al , 2021).…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Dynamic model to characterise sectors using machine learning techniques

Moreno

Comendador

Jurado

et al. 2022

AEAT

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Purpose The purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based. Design/methodology/approach The methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows. Findings The results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate. Research limitations/implications The methodology is in its preliminary phase and has been tested with very little data. Further refinement is required. Originality/value The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly.

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“…Another research on this subject was included in the work [3] where the author described a problem of air routing and discussed the use of algorithms/approaches in the conventional Air Traffic Services (ATS) route network and FRA environment. In work [4], there are several graph-based airspace models, for more efficient free route planning. These graph models use the FRA concept.…”

Section: Introductionmentioning

confidence: 99%

A Spatially Bounded Airspace Axiom

Szabó

Ferencová

Blišťanová

2022

Axioms

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Free Route Airspace (FRA), a new concept implemented across European airspace, is designed to eliminate the adverse effects of air traffic, reduce fuel consumption, simplify and expand flight planning. In our research, we model FRA system by using graph theory and we use networks and convex analysis to study it. We work with 2-dimensional space defined by latitude a longitude. We know that the basic mathematical properties of a scientific discipline can be expressed using mathematical axioms. Therefore, in our work, we sought to answer the question, what are the basic mathematical properties of airspace? We found a mathematical object that can describe airspace in general. Using this object, we uttered an axiom describing airspace. Our axiom is given by means of practical, significant entry and entry-exit points in the airspace.

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Regular graph-based free route flight planning approach

Cited by 11 publications

References 19 publications

Safety performance functions to predict separation minima infringements in en-route airspace

Safety performance functions to predict separation minima infringements in en-route airspace

Dynamic model to characterise sectors using machine learning techniques

A Spatially Bounded Airspace Axiom

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