A Novel Performance Framework and Methodology to Analyze the Impact of 4D Trajectory Based Operations in the Future Air Traffic Management System

Ruiz, Sergio; Leonés, Javier López; Ranieri, Andrea

doi:10.1155/2018/1601295

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…In order to continue to meet the different expectations of various stakeholders in terms of the comprehensive performance of operational efficiency, flight safety, cost-effectiveness, and environmental impact, many countries and organizations have initiated various types of air traffic system upgrade projects to cope with the ever-growing demand for air traffic, complex air traffic systems, and diverse operating environments [2]. Whether it is the current sector-based operation mode or the future trajectory-based operation mode, the prediction of the future trajectory of the aircraft is the key to the efficient operation of these two modes.…”

Section: Introductionmentioning

confidence: 99%

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng

Chu

et al. 2022

Aerospace

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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.

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Section: Introductionmentioning

confidence: 99%

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng

Chu

et al. 2022

Aerospace

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“…Several authors point out that complex interdependencies among the decision layers can cause unnecessary penalization of some KPAs to improve others. In [3], the authors claim as a result of early SESAR projects such as STREAM [4], that the exact relationship among these KPAs is still not well-understood and should be further studied in future research. Moreover, recent finalized projects such as APACHE [5] targeting the analysis of the interdependencies between the different KPAs by capturing the Pareto-front of ATM still claim that further research is needed to uncover the inter-dependencies between the different KPAs [6].…”

Section: Introductionmentioning

confidence: 99%

From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics

et al. 2022

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At present, decision-making in ATM is fragmented between different stakeholders who have different objectives. This fragmentation, in unison with competing KPAs, leads to complex interdependencies between performance indicators, which results in an imbalance, with some of these indicators being penalized to the apparent benefit of others. Therefore, it is necessary to support ATM stakeholders in systematically uncovering hidden trade-offs between KPAs. Existing literature confirms this claim, but how to solve it has not been fully addressed. In this paper, we envision air traffic complexity to be the framework through which a common understanding among stakeholders is enhanced. We introduce the concept of single aircraft complexity to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize and improve decision-making. Further, we illustrates how the provided information can be used to increase transparency of the decision makers towards different airspace users. In order to showcase the methodology, we develop a tool that visualizes different outputs of the algorithm. Lastly, we conduct sensitivity analysis in order to systematically analyse how each input affects the methodology.

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“…ATM systems are evolving towards an operational concept known as Trajectory Based Operations (TBO) that aims to enable a more coordinated decision making process between the different stakeholders. Such systems will rely on human centered automation schemes with widespread use of Decision Support Tools (DSTs), each of which containing an accurate trajectory predictor [5,6]. An in-depth description of the components of trajectory prediction including the atmosphere model, together with their importance in the reduction of the ATM system uncertainty, is presented in [7].…”

Section: Introduction -Influence Of Atmosphere In Trajectory Predictionmentioning

confidence: 99%

Quasi Static Atmospheric Model for Aircraft Trajectory Prediction and Flight Simulation

Gallo

2021

Preprint

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Aircraft trajectory prediction requires the determination of the atmospheric properties (pressure, temperature, and density) encountered by the aircraft during its flight. This is accomplished by employing a tabulated prediction published by a meteorological service, a static atmosphere model that does not consider the atmosphere variation with time or horizontal position, such as the International Civil Aviation Organization (ICAO) Standard Atmosphere (ISA), or a variation to the later so it better conforms with the expected flight conditions. This article proposes an easy-to-use quasi static model that introduces temperature and pressure variations while respecting all the hypotheses of the ISA model, resulting in more realistic trajectory predictions as the obtained atmospheric properties bear a higher resemblance with the local conditions encountered during the flight. The proposed model relies on two parameters, the temperature and pressure offsets, and converges to the ISA model when both are zero. Expressions to obtain the atmospheric properties are established, and their dependencies with both parameters explained. The author calls this model INSA (ICAO Non Standard Atmosphere) and releases its C ++ implementation as open-source software [1]. * The author holds a MSc in Aerospace Engineering by the Polytechnic University of Madrid and has twenty-two years of experience working in aircraft trajectory prediction, modeling, and flight simulation. He is currently a Senior Trajectory Prediction and Aircraft Performance Engineer at Boeing Research & Technology Europe (BR&TE), although he is publishing this article in his individual capacity and time as part of his PhD thesis titled "Autonomous Unmanned Air Vehicle GNSS-Denied Navigation", advised by Dr. Antonio Barrientos within the Centre for Automation and Robotics of the Polytechnic University of Madrid.

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A Novel Performance Framework and Methodology to Analyze the Impact of 4D Trajectory Based Operations in the Future Air Traffic Management System

Cited by 9 publications

References 12 publications

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics

Quasi Static Atmospheric Model for Aircraft Trajectory Prediction and Flight Simulation

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