Prediction of aircraft performances based on data collected by air traffic control centers

Hrastovec, Marko; Solina, Franc

doi:10.1016/j.trc.2016.10.018

Cited by 38 publications

(16 citation statements)

References 15 publications

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“…A different approach to model aircraft performance is to use flight data. An recent study of Hrastovec and Solina (2016) proposes to obtain performance parameters and patterns using radar data and machine learning methods. At the same time, thanks to the mandates on aircraft Automatic Dependent Surveillance-Broadcast (ADS-B) transponders from different regulatory agencies around the world, an increasing number of commercial aircraft are being equipped with this capability.…”

Section: Introductionmentioning

confidence: 99%

WRAP: An open-source kinematic aircraft performance model

Sun

Ellerbroek

Hoekstra

2019

Transportation Research Part C: Emerging Technologies

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Open access to flight data from Automatic Dependent Surveillance-Broadcast (ADS-B) has provided researchers with more insights for air traffic management than aircraft tracking alone. With large quantities of trajectory data collected from a wide range of different aircraft types, it is possible to extract accurate aircraft performance parameters. In this paper, a set of more than thirty parameters from seven distinct flight phases are extracted for common commercial aircraft types. It uses various data mining methods, as well as a maximum likelihood estimation approach to generate parametric models for these performance parameters. All parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing. Both analytical results and summaries are shown. When available, optimal parameters from these models are also compared with the Base of Aircraft Data and the Eurocontrol aircraft performance database. This research presents a comprehensive set of methods for extracting different aircraft performance parameters. It also provides the first set of open parametric performance data for common aircraft types. All model data are published as open data under a flexible open-source license.

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Section: Introductionmentioning

confidence: 99%

WRAP: An open-source kinematic aircraft performance model

Sun

Ellerbroek

Hoekstra

2019

Transportation Research Part C: Emerging Technologies

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“…Summing up, in this paper the path constraints that can close the mathematical problem are given by Eqs. (3),(4), (6) and (8) and the parameters vector is composed by p = [π, VS, γ g , k]. Each guidance mode will be defined by a combination two of these constraints and will be parameterised in terms of p.…”

Section: A Aircraft Dynamics Modelmentioning

confidence: 99%

“…A similar limitation is found for ground-based TPs, which typically use the Airline Procedure Model (ARPM), embedded in the Base of Aircraft Data (BADA) [5]. The ARPM, however, tends to be too generalist for most applications [6].…”

Section: Introductionmentioning

confidence: 97%

“…The paper is focused in aircraft climbing or descending, which are more challenging to track and predict, and considers non-standard atmospheric conditions. A clear application of this methodology would be to provide accurate and reliable flight-intent data to any kind of airborne or ground-based TP, but would also enable a wide range of ATM performance studies, ATM analytics [6].…”

Section: Introductionmentioning

confidence: 99%

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Real-time Identification of Guidance Modes in Aircraft Descents Using Surveillace Data

Codina

Batlle

Menéndez

2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

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Trajectory predictors require information on the flight-intent in order to estimate the future state of the aircraft. At present, however, such information is not available or it is very limited and coarse (unless predicting the ownship trajectory). In this paper, an interacting multiple-model (IMM) algorithm is proposed to improve the accuracy of short-term trajectory predictions. The active guidance mode of an aircraft is estimated in real-time observing flight data collected only from automatic dependent surveillance-Broadcast (ADS-B) and transponder selective mode (Mode S) emissions. The algorithm is set up with different models corresponding to the most typical guidance modes, and provides the model that better fits the observations. The proposed algorithm is validated by means of two simulated trajectories whose guidance modes were known beforehand. Finally, the performance of the algorithm with real flight data is demonstrated through a detailed example. Promising results are obtained, showing that the active guidance mode can be unequivocally identified with a negligible delay.

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“…As a part of our previous study, it was also applied to check the conformance of the flight trajectory [3], which ensures the safety of the flight operation. Machine learning-based algorithms were proposed to cope with the high stochasticity of the flight environment [4][5][6], typically a sequential regression task. In general, existing approaches can be summarized as the following categories:…”

Section: Introductionmentioning

confidence: 99%

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

2020

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In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

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Prediction of aircraft performances based on data collected by air traffic control centers

Cited by 38 publications

References 15 publications

WRAP: An open-source kinematic aircraft performance model

WRAP: An open-source kinematic aircraft performance model

Real-time Identification of Guidance Modes in Aircraft Descents Using Surveillace Data

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

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