Design of a conflict-detection air traffic control tool for the implementation of continuous climb operations: A case study at Palma TMA

Pérez–Castán, Javier A.; Comendador, Fernando Gómez; Rodríguez–Sanz, Álvaro; Barragán, Rocío; Arnaldo-Valdés, Rosa María

doi:10.1177/0954410019831198

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Paielli et al [14] developed a new approach based on tactical pairwise trajectory analysis. Other authors found their studies on complex probabilistic models to understand trajectory uncertainty [15][16][17][18].…”

Section: Of 18mentioning

confidence: 99%

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

et al. 2022

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Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.

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Section: Of 18mentioning

confidence: 99%

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

et al. 2022

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“…Paielli et al [ 18 ] developed a new approach based on tactical pairwise trajectory analysis. Other authors found their studies on complex probabilistic models to understand trajectory uncertainty [ 19 , 20 , 21 ].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor

Pérez–Castán

Sanz

Fernández-Castellano

et al. 2022

Sensors

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Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time.

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“…The blocking-area allows knowing if an aircraft influences the trajectory of other aircraft and may imply a modification of the other trajectory. When the blocking-area transforms its application from location to time, it evolves to a temporary-blocking window [24][25][26]. In this case, the temporary-blocking window provides the time span an aircraft cannot enter in the critical section because one conflict will arise.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Strategic Conflict-Management for 4D Trajectories in Free-Route Airspace

Pérez–Castán

Rodríguez–Sanz

Sanz

et al. 2020

Entropy

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The expected growth of air traffic in the following decades demands the implementation of new operational concepts to avoid current limitations of the air traffic management system. This paper focuses on the strategic conflict management for four-dimensional trajectories (4DT) in free-route airspace. 4DT has been proposed as the future operational concept to manage air traffic. Thus, aircraft must fulfil temporary restrictions at specific waypoints in the airspace based on time windows. Based on the temporary restrictions, a strategic conflict management method is proposed to calculate the conflict probability of an aircraft pair (that intersects in the air) and to calculate temporary-blocking windows that quantify the time span at which an aircraft cannot depart because one conflict could occur. This methodology was applied in a case-study for an aircraft pair, including the uncertainty associated with 4DT. Moreover, a sensitivity analysis was performed to characterise the impact of wind conditions and speed control on the temporary-blocking windows. The results concluded that it is feasible to propose 4DT strategic de-confliction based on temporary-blocking windows. Although, uncertainty variables such as wind and speed control impact on the conflict probability and the size of the temporary-blocking windows.

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Design of a conflict-detection air traffic control tool for the implementation of continuous climb operations: A case study at Palma TMA

Cited by 4 publications

References 21 publications

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor

Probabilistic Strategic Conflict-Management for 4D Trajectories in Free-Route Airspace

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