Causal Analysis of Airline Trajectory Preferences to Improve Airspace Capacity

Schefers, Nina; Piera, Miquel Àngel; Ramos, Juan Jos; Nosedal, Jenaro

doi:10.1016/j.procs.2017.01.141

Cited by 6 publications

(4 citation statements)

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“…The distance-based definition has been used in different research such as (Ayhan et al, 2018;Erzberger et al, 1997;Hernández-Romero et al, 2019;Liu and Hwang, 2011;Paielli and Erzberger, 2008;Piroddi and Prandini, 2010;Schefers et al, 2017). In addition, the modelling of the time separation as a folded normal distribution was similar to the one presented by (Irvine, 2001), but for time-based separations.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Gallego

Comendador

Carmona

et al. 2019

Transportation Research Part C: Emerging Technologies

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Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Gallego

Comendador

Carmona

et al. 2019

Transportation Research Part C: Emerging Technologies

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show abstract

“…It is also influenced by numerous other factors like the disposition of aircraft in the airspace [160], relative aircraft velocity, aircraft heading, aircraft types, airspace structure, weather, and availability/quality of ATC equipments [161]. Sometimes the controller in charge of an operational sector can accept more aircraft than the actual capacity, while on some occasions will refuse some flights even though the capacity has not been reached [162]. Thus, the controller's decision about the capacity overload can not be summarized by how many aircraft can be maintained in the airspace, though they are correlated.…”

Section: Qualitative Methods: Other Capacity Overload Indicatorsmentioning

confidence: 99%

Data-driven network modeling for airspace optimization through airway re-structurization and traffic flow prediction

Ma¹

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“…At tactical level, ATC complexity is impacted by trajectory uncertainty and vice versa (as argued in the previous point) [62]. At tactical planning level, the system capacity and demand is affected by the granular capacity levels of the airspace, driven mainly by ATC complexity (hotspots) and by the airport processes [76]. Finally, the strategic organisation of ATM is impacted by the organisation of all the processes defining ATM and their defined capacities.…”

Section: A Complex Systemmentioning

confidence: 99%

“…A spatial dimension can aggregate those by means of regions, where the smallest grain of the dimension contains spatially these points. Regions could be organised through regular and independent partitions, such as regular grids as proposed by [104] or [76] and applied in [105]- [107], or irregular and system-oriented ones, such as airspace volumes or for the operational side of the ATM. now, they can be identified as spatial entities.…”

Section: Weekmentioning

confidence: 99%

Extraction of trajectory contextual factors and its application in the trajectory prediction using statistical methods

Gallego¹

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As stated by Schuster and Ochieng in [4], "a lack of understanding of TP and Conflict Detection and Resolution driver uncertainties (especially contextual, and aircraft state, operational and performance uncertainties)" was one the primary limitations of TP tools supporting functionalities envisaged in the initial SESAR Concept of Operations (ConOps)[13].TP's performance is dependent on the input data. In an aircraft-centric trajectory predictor, the input data are the initial state, aircraft-specific parameters, some contextual factors such as meteorological conditions and operational constraints, and finally, the aircraft-intent information.

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Causal Analysis of Airline Trajectory Preferences to Improve Airspace Capacity

Cited by 6 publications

References 1 publication

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Data-driven network modeling for airspace optimization through airway re-structurization and traffic flow prediction

Extraction of trajectory contextual factors and its application in the trajectory prediction using statistical methods

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